Keyword Extraction
32 papers with code • 3 benchmarks • 7 datasets
Keyword extraction is tasked with the automatic identification of terms that best describe the subject of a document (Source: Wikipedia).
Benchmarks
These leaderboards are used to track progress in Keyword Extraction
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Datasets
MPQA Opinion Corpus
SemEval-2017 Task-10
Most implemented papers
sCAKE: Semantic Connectivity Aware Keyword Extraction
Combination of the proposed graph construction and scoring methods leads to a novel, parameterless keyword extraction method (sCAKE) based on semantic connectivity of words in the document.
Complex Network based Supervised Keyword Extractor
This shows that the proposed method is independent of the domain, collection, and language of the training corpora.
Combining Graph Degeneracy and Submodularity for Unsupervised Extractive Summarization
We present a fully unsupervised, extractive text summarization system that leverages a submodularity framework introduced by past research.
YAKE! Keyword extraction from single documents using multiple local features
In this paper, we present YAKE!, a novel feature-based system for multi-lingual keyword extraction from single documents, which supports texts of different sizes, domains or languages.
Efficient Generation and Processing of Word Co-occurrence Networks Using corpus2graph
Corpus2graph is an open-source NLP-application-oriented tool that generates a word co-occurrence network from a large corpus.
RaKUn: Rank-based Keyword extraction via Unsupervised learning and Meta vertex aggregation
Keyword extraction is used for summarizing the content of a document and supports efficient document retrieval, and is as such an indispensable part of modern text-based systems.
Semantic Sensitive TF-IDF to Determine Word Relevance in Documents
vfcarida/Semantic-Sensitive-TF-IDF-to-Determine-Word-Relevance-in-Documents
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Keyword extraction has received an increasing attention as an important research topic which can lead to have advancements in diverse applications such as document context categorization, text indexing and document classification.
TNT-KID: Transformer-based Neural Tagger for Keyword Identification
With growing amounts of available textual data, development of algorithms capable of automatic analysis, categorization and summarization of these data has become a necessity.
Keywords lie far from the mean of all words in local vector space
Keyword extraction is an important document process that aims at finding a small set of terms that concisely describe a document's topics.
